The log-likelihood function for the logistic regression objective function. More...

Public Member Functions
	LogisticRegressionFunction (const MatType &predictors, const arma::Row< size_t > &responses, const double lambda=0)

	LogisticRegressionFunction (const MatType &predictors, const arma::Row< size_t > &responses, const arma::vec &initialPoint, const double lambda=0)

double	Evaluate (const arma::mat &parameters) const
	Evaluate the logistic regression log-likelihood function with the given parameters. More...

double	Evaluate (const arma::mat &parameters, const size_t i) const
	Evaluate the logistic regression log-likelihood function with the given parameters, but using only one data point. More...

const arma::mat &	GetInitialPoint () const
	Return the initial point for the optimization. More...

void	Gradient (const arma::mat &parameters, arma::mat &gradient) const
	Evaluate the gradient of the logistic regression log-likelihood function with the given parameters. More...

void	Gradient (const arma::mat &parameters, const size_t i, arma::mat &gradient) const
	Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one point in the dataset. More...

const arma::mat &	InitialPoint () const
	Return the initial point for the optimization. More...

arma::mat &	InitialPoint ()
	Modify the initial point for the optimization. More...

const double &	Lambda () const
	Return the regularization parameter (lambda). More...

double &	Lambda ()
	Modify the regularization parameter (lambda). More...

size_t	NumFunctions () const
	Return the number of separable functions (the number of predictor points). More...

const MatType &	Predictors () const
	Return the matrix of predictors. More...

const arma::vec &	Responses () const
	Return the vector of responses. More...

Private Attributes
arma::mat	initialPoint
	The initial point, from which to start the optimization. More...

double	lambda
	The regularization parameter for L2-regularization. More...

const MatType &	predictors
	The matrix of data points (predictors). More...

const arma::Row< size_t > &	responses
	The vector of responses to the input data points. More...

Detailed Description

template<typename MatType = arma::mat>
class mlpack::regression::LogisticRegressionFunction< MatType >

The log-likelihood function for the logistic regression objective function.

This is used by various mlpack optimizers to train a logistic regression model.

Definition at line 28 of file logistic_regression_function.hpp.

Constructor & Destructor Documentation

template<typename MatType = arma::mat>

mlpack::regression::LogisticRegressionFunction< MatType >::LogisticRegressionFunction	(	const MatType &	predictors,
		const arma::Row< size_t > &	responses,
		const double	lambda = `0`
	)

template<typename MatType = arma::mat>

mlpack::regression::LogisticRegressionFunction< MatType >::LogisticRegressionFunction	(	const MatType &	predictors,
		const arma::Row< size_t > &	responses,
		const arma::vec &	initialPoint,
		const double	lambda = `0`
	)

Member Function Documentation

template<typename MatType = arma::mat>

double mlpack::regression::LogisticRegressionFunction< MatType >::Evaluate ( const arma::mat & parameters ) const

Evaluate the logistic regression log-likelihood function with the given parameters.

Note that if a point has 0 probability of being classified directly with the given parameters, then Evaluate() will return nan (this is kind of a corner case and should not happen for reasonable models).

The optimum (minimum) of this function is 0.0, and occurs when each point is classified correctly with very high probability.

Parameters

parameters Vector of logistic regression parameters.

Referenced by mlpack::regression::LogisticRegressionFunction< MatType >::Responses().

template<typename MatType = arma::mat>

double mlpack::regression::LogisticRegressionFunction< MatType >::Evaluate	(	const arma::mat &	parameters,
		const size_t	i
	)		const

Evaluate the logistic regression log-likelihood function with the given parameters, but using only one data point.

This is useful for optimizers such as SGD, which require a separable objective function. Note that if the point has 0 probability of being classified correctly with the given parameters, then Evaluate() will return nan (this is kind of a corner case and should not happen for reasonable models).

The optimum (minimum) of this function is 0.0, and occurs when the point is classified correctly with very high probability.

Parameters

parameters	Vector of logistic regression parameters.
i	Index of point to use for objective function evaluation.

template<typename MatType = arma::mat>

const arma::mat& mlpack::regression::LogisticRegressionFunction< MatType >::GetInitialPoint ( ) const

inline

Return the initial point for the optimization.

Definition at line 108 of file logistic_regression_function.hpp.

References mlpack::regression::LogisticRegressionFunction< MatType >::initialPoint.

template<typename MatType = arma::mat>

void mlpack::regression::LogisticRegressionFunction< MatType >::Gradient	(	const arma::mat &	parameters,
		arma::mat &	gradient
	)		const

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters.

Parameters

parameters	Vector of logistic regression parameters.
gradient	Vector to output gradient into.

Referenced by mlpack::regression::LogisticRegressionFunction< MatType >::Responses().

template<typename MatType = arma::mat>

void mlpack::regression::LogisticRegressionFunction< MatType >::Gradient	(	const arma::mat &	parameters,
		const size_t	i,
		arma::mat &	gradient
	)		const

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one point in the dataset.

This is useful for optimizers such as SGD, which require a separable objective function.

Parameters

parameters	Vector of logistic regression parameters.
i	Index of points to use for objective function gradient evaluation.
gradient	Vector to output gradient into.

template<typename MatType = arma::mat>

const arma::mat& mlpack::regression::LogisticRegressionFunction< MatType >::InitialPoint ( ) const

inline

Return the initial point for the optimization.

Definition at line 41 of file logistic_regression_function.hpp.

References mlpack::regression::LogisticRegressionFunction< MatType >::initialPoint.

template<typename MatType = arma::mat>

arma::mat& mlpack::regression::LogisticRegressionFunction< MatType >::InitialPoint ( )

inline

Modify the initial point for the optimization.

Definition at line 43 of file logistic_regression_function.hpp.

References mlpack::regression::LogisticRegressionFunction< MatType >::initialPoint.